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Application of Cloud Computing in Industrial Internet of Things
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Application of Cloud Computing in Industrial Internet of Things

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5957

Special Issue Editors

Dr. Muhammad Kazim

E-Mail Website
Guest Editor
Cyber Technology Institute, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Interests: cloud computing; internet of things; industrial internet of things; edge computing; edge AI; AI in cyber security
Dr. Mujeeb Ur Rehman

E-Mail
Guest Editor
Cyber Technology Institute, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Interests: machine learning; deep learning; security and privacy; internet of things; cryptography
Special Issues, Collections and Topics in MDPI journals
Dr. Stefan Kuhn

E-Mail Website
Guest Editor
Institute of Computer Science, University of Tartu, Narva Mnt. 18, 51009 Tartu, Estonia
Interests: chemoinformatics; distributed systems; software engineering

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has become a global network that interconnects an extensive range of devices, enabling them to collect and exchange data, thereby facilitating advancements across a large number of sectors. The Industrial Internet of Things (IIoT) builds upon this interconnected fabric, specifically tailored to industrial environments, where it enhances operational efficiency by integrating machine learning and big data technology to harness sensor data, machine-to-machine (M2M) communication, and automation technologies. Cloud computing serves as the cornerstone of the IIoT infrastructure, providing a dynamic, scalable, and powerful platform for data processing and analytics. The integration of cloud computing into the IIoT paradigm is instrumental in reducing decision-making latency, optimising resources, and enhancing privacy protection.

This Special Issue invites academics, professionals, and business experts to exchange knowledge in this rapidly growing field. It comprehensively covers the most recent developments in the closely linked topics of Industrial Internet of Things and cloud computing.

Researchers are encouraged to contribute their unpublished original findings on the following topics, although they are not limited to them:

  • Cloud-based big data analytics and machine learning in IIoT;
  • Cloud service models for IIoT device management;
  • Digital twins using the cloud for IIoT;
  • Security and privacy of cloud-based IIoT;
  • Energy-efficient cloud computing in IIoT;
  • Impact of 5G/6G on cloud-integrated IIoT;
  • Blockchain as a cloud service for IIoT;
  • Edge computing in IIoT;
  • Cloud-driven supply chain optimisation in IIoT;
  • Optimisation of algorithms for cloud-based IIoT systems;
  • Cloud-enhanced smart manufacturing and robotics.

We look forward to receiving your contributions. 

Dr. Muhammad Kazim
Dr. Mujeeb Ur Rehman
Dr. Stefan Kuhn
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cloud computing
  • internet of things
  • industrial internet of things
  • security and privacy
  • big data analytics
  • machine learning
  • digital twins
  • supply chain optimisation
  • smart manufacturing and robotics
  • blockchain
  • edge computing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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21 pages, 2085 KiB  
Article
Edge vs. Cloud: Empirical Insights into Data-Driven Condition Monitoring
by Chikumbutso Christopher Walani and Wesley Doorsamy
Big Data Cogn. Comput. 2025, 9(5), 121; https://doi.org/10.3390/bdcc9050121 - 8 May 2025
Viewed by 418
Abstract
This study evaluates edge and cloud computing paradigms in the context of data-driven condition monitoring of rotating electrical machines. Two well-known platforms, the Raspberry Pi and Amazon Web Services Elastic Compute Cloud, are used to compare and contrast these two computing paradigms in [...] Read more.
This study evaluates edge and cloud computing paradigms in the context of data-driven condition monitoring of rotating electrical machines. Two well-known platforms, the Raspberry Pi and Amazon Web Services Elastic Compute Cloud, are used to compare and contrast these two computing paradigms in terms of different metrics associated with their application suitability. The tested induction machine fault diagnosis models are developed using popular algorithms, namely support vector machines, k-nearest neighbours, and decision trees. The findings reveal that while the cloud platform offers superior computational and memory resources, making it more suitable for complex machine learning tasks, it also incurs higher costs and latency. On the other hand, the edge platform excels in real-time processing and reduces network data burden, but its computational and memory resources are found to be a limitation with certain tasks. The study provides both quantitative and qualitative insights into the trade-offs involved in selecting the most suitable computing approach for condition monitoring applications. Although the scope of the empirical study is primarily limited to factors such as computational efficiency, scalability, and resource utilisation, particularly in the context of specific machine learning models, this paper offers broader discussion and future research directions of other key issues, including latency, network variability, and energy consumption. Full article
(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things)
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14 pages, 2950 KiB  
Article
3D Urban Digital Twinning on the Web with Low-Cost Technology: 3D Geospatial Data and IoT Integration for Wellness Monitoring
by Marcello La Guardia
Big Data Cogn. Comput. 2025, 9(4), 107; https://doi.org/10.3390/bdcc9040107 - 21 Apr 2025
Viewed by 540
Abstract
Recent advances in computer science and geomatics have enabled the digitalization of complex two-dimensional and three-dimensional spatial environments and the sharing of geospatial data on the web. Simultaneously, the widespread adoption of Internet of Things (IoT) technology has facilitated the rapid deployment of [...] Read more.
Recent advances in computer science and geomatics have enabled the digitalization of complex two-dimensional and three-dimensional spatial environments and the sharing of geospatial data on the web. Simultaneously, the widespread adoption of Internet of Things (IoT) technology has facilitated the rapid deployment of low-cost sensor networks in various scientific applications. The integration of real-time IoT data acquisition in 3D urban environments lays the foundation for the development of Urban Digital Twins. This work proposes a possible low-cost solution as a sample of a structure for 3D digital twinning on the web, presenting a case study related to weather monitoring analysis. Specifically, an indoor-outdoor environmental conditions monitoring system integrated with 3D geospatial data on a 3D WebGIS platform was developed. This solution can be considered as a first step for monitoring human and environmental wellness within a geospatial analysis system that integrates several open-source modules that provide different kinds of information (geospatial data, 3D models, and IoT acquisition). The structure of this system can be valuable for municipalities and private stakeholders seeking to conduct environmental geospatial analysis using cost-effective solutions. Full article
(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things)
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23 pages, 5219 KiB  
Article
Optimized Resource Allocation Algorithm for a Deadline-Aware IoT Healthcare Model
by Amal EL-Natat, Nirmeen A. El-Bahnasawy, Ayman El-Sayed and Sahar Elkazzaz
Big Data Cogn. Comput. 2025, 9(4), 80; https://doi.org/10.3390/bdcc9040080 - 30 Mar 2025
Viewed by 268
Abstract
In recent years, the healthcare market has grown very fast and is dealing with a huge increase in data. Healthcare applications are time-sensitive and need quick responses with fewer delays. Fog Computing (FC) was introduced to achieve this aim. It can be applied [...] Read more.
In recent years, the healthcare market has grown very fast and is dealing with a huge increase in data. Healthcare applications are time-sensitive and need quick responses with fewer delays. Fog Computing (FC) was introduced to achieve this aim. It can be applied in various application areas like healthcare, smart and intelligent environments, etc. In healthcare applications, some tasks are considered critical and need to be processed first; other tasks are time-sensitive and need to be processed before their deadline. In this paper, we have proposed a Task Classification algorithm based on Deadline and Criticality (TCDC) for serving healthcare applications in a fog environment. It depends on classifying tasks based on the critical level to process critical tasks first and considers the deadline of the task, which is an essential parameter to consider in real-time applications. The performance of TCDC was compared with some of the literature. The simulation results showed that the proposed algorithm can improve the overall performance in terms of some QoS parameters like makespan with an improved ratio from 60% to 70%, resource utilization, etc. Full article
(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things)
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21 pages, 7488 KiB  
Article
Low-Cost Embedded System Applications for Smart Cities
by Victoria Alejandra Salazar Herrera, Hugo Puertas de Araújo, César Giacomini Penteado, Mario Gazziro and João Paulo Carmo
Big Data Cogn. Comput. 2025, 9(2), 19; https://doi.org/10.3390/bdcc9020019 - 22 Jan 2025
Viewed by 1397
Abstract
The Internet of Things (IoT) represents a transformative technology that allows interconnected devices to exchange data over the Internet, enabling automation and real-time decision making in a variety of areas. A key aspect of the success of the IoT lies in its integration [...] Read more.
The Internet of Things (IoT) represents a transformative technology that allows interconnected devices to exchange data over the Internet, enabling automation and real-time decision making in a variety of areas. A key aspect of the success of the IoT lies in its integration with low-resource hardware, such as low-cost microprocessors and microcontrollers. These devices, which are affordable and energy efficient, are capable of handling basic tasks such as sensing, processing, and data transmission. Their low cost makes them ideal for IoT applications in low-income communities where the government is often absent. This review aims to present some applications—such as a flood detection system; a monitoring system for analog and digital sensors; an air quality measurement system; a mesh video network for community surveillance; and a real-time fleet management system—that use low-cost hardware such as ESP32, Raspberry Pi, and Arduino, and the MQTT protocol used to implement low-cost monitoring systems applied to improve the quality of life of people in small cities or communities. Full article
(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things)
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Review

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32 pages, 498 KiB  
Review
A Survey on the Applications of Cloud Computing in the Industrial Internet of Things
by Elias Dritsas and Maria Trigka
Big Data Cogn. Comput. 2025, 9(2), 44; https://doi.org/10.3390/bdcc9020044 - 17 Feb 2025
Cited by 1 | Viewed by 2454
Abstract
The convergence of cloud computing and the Industrial Internet of Things (IIoT) has significantly transformed industrial operations, enabling intelligent, scalable, and efficient systems. This survey provides a comprehensive analysis of the role cloud computing plays in IIoT ecosystems, focusing on its architectural frameworks, [...] Read more.
The convergence of cloud computing and the Industrial Internet of Things (IIoT) has significantly transformed industrial operations, enabling intelligent, scalable, and efficient systems. This survey provides a comprehensive analysis of the role cloud computing plays in IIoT ecosystems, focusing on its architectural frameworks, service models, and application domains. By leveraging centralized, edge, and hybrid cloud architectures, IIoT systems achieve enhanced real-time processing capabilities, streamlined data management, and optimized resource allocation. Moreover, this study delves into integrating artificial intelligence (AI) and machine learning (ML) in cloud platforms to facilitate predictive analytics, anomaly detection, and operational intelligence in IIoT environments. Security challenges, including secure device-to-cloud communication and privacy concerns, are addressed with innovative solutions like blockchain and AI-powered intrusion detection systems. Future trends, such as adopting 5G, serverless computing, and AI-driven adaptive services, are also discussed, offering a forward-looking perspective on this rapidly evolving domain. Finally, this survey contributes to a well-rounded understanding of cloud computing’s multifaceted aspects and highlights its pivotal role in driving the next generation of industrial innovation and operational excellence. Full article
(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things)
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